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Empty Seats,
Full Streets Fixing Manhattan's Traffic Problem
DECEMBER 21, 2017
SCHALLER CONSULTING
94 Windsor Place, Brooklyn NY 11215
718 768 3487
www.schallerconsult.com
This report was prepared by Bruce Schaller, Principal of Schaller Consulting. An expert on issues surrounding
the rise of new mobility services in major U.S. cities, Mr. Schaller served as Deputy Commissioner for Traffic and
Planning at the New York City Department of Transportation and Policy Director at the NYC Taxi and Limousine
Commission, and has consulted on transportation policy across the United States. He is the author of the
February 2017 report, "Unsustainable? The Growth of App-Based Ride Services and Traffic, Travel and the
Future of New York City," and co-author of a 2015 National Academy of Sciences report on emerging mobility
providers. He also served as an Advisor for the City of New York's study of for-hire vehicle issues. He has been
called "a widely acknowledged expert" on issues related to taxis, Uber and Lyft (Politico) and a "nationally
recognized expert in the cab business" (Washington Post). Mr. Schaller has published extensively in peer-
reviewed academic journals including Transport Policy, Transportation and the Journal of Public Transportation.
This report was researched and written by Mr. Schaller for the purpose of furthering public understanding and
discussion of the role that app-based ride services and other vehicle-for-hire services can and should play in
furthering urban mobility, safety and environmental goals. The author would like to thank staff at the Taxi and
Limousine Commission and NYC Department of Transportation who provided information and insight for the
analysis. The analysis and conclusions are the sole responsibility of the author.
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Contents
Summary ....................................................................................................................................................................................................... 1
Introduction ................................................................................................................................................................................................. 3
1. Methodology ........................................................................................................................................................................................... 4
2. Findings .................................................................................................................................................................................................... 6
3. Policy Options....................................................................................................................................................................................... 13
4. Conclusion ............................................................................................................................................................................................. 19
Appendix: Results by Time of Day .................................................................................................................................................... 21
Endnotes ..................................................................................................................................................................................................... 23
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EMPTY SEATS, FULL STREETS 1
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Summary
The rapid growth of app-based ride services such as Uber and
Lyft has raised concerns in large U.S. cities such as New York.
San Francisco, Chicago and Seattle about their impacts on
traffic congestion and public transportation ridership. In New
York City, the growth of app-based ride services (often called
"Transportation Network Companies," or TNCs) has raised
questions about how anti-congestion plans being developed by
Governor Andrew Cuomo and Mayor Bill de Blasio should
address TNCs' contributions to traffic congestion.
This report examines the impact of TNC growth on traffic
conditions in the Manhattan Central Business District (CBD),
defined as 60 Street to the Battery, river to river. Using newly
available data on TNC trips, the report presents a more
detailed analysis of CBD traffic conditions than has been
possible previously, isolating the impact of TNC growth in the
Manhattan CBD during the most congested part of the day --
weekdays between 8 a.m. and 7 p.m.
The analysis takes account both the rapid growth of TNCs and
declines in yellow cab activity, thus focusing on net growth of
the combined taxi/TNC sector. Key findings are:
Taxi/TNC trips increased by 15 percent on the average
weekday in June 2017 compared with June 2013.
Total taxi/TNC weekday mileage in the CBD increased by
36 percent from 2013 to 2017. Mileage increased more
rapidly than trips due to a trend toward longer trips and
lower utilization rates (percentage of mileage with
passengers).
The number of taxi/TNC vehicles in the CBD increased by
59 percent. Vehicles increased more rapidly than mileage
due to slower traffic speeds.
Total hours spent transporting passengers increased by 48
percent over the last four years, slightly less than the
overall growth of vehicles in the CBD because utilization
rates declined.
The number of unoccupied taxi/TNC vehicles increased
by 81 percent, more rapidly than overall vehicle hours due
to declining utilization rates.
Increases in trips, mileage and the number of vehicles in
the CBD vary considerably during the day. The largest
increases were from 4 p.m. to 6 p.m.., with the number of
taxi/TNC vehicles more than doubling during this time
period.
Large increases in the number of taxi/TNC vehicles in the CBD
are an important source of slow traffic conditions in the
Manhattan CBD. The very rapid growth in unoccupied
vehicles in the CBD is of particular note since the increased
time and mileage that drivers spend between trips exacerbates
congestion but does not contribute to the mobility needs of
New Yorkers. Reducing unoccupied time thus presents an
opportunity to reduce Manhattan traffic congestion and
improve both mobility (through less congested traffic) as well
as driver incomes (through less time waiting for the next trip).
This report focuses on ways to reduce unoccupied time in the
CBD by taxis and TNCs. The most promising option is for the
City or State to mandate that Uber, Lyft and other TNCs limit
the time that their drivers spend waiting for their next trip
request, which now averages 11 minutes between trips. TNCs
already utilize dispatch methods at airports across the country
that could dramatically shorten unoccupied time between trips
if utilized for dispatching CBD trips.
Yellow cabs could also be mandated to reduce their
unoccupied time between trips. Since they predominantly
respond to street hail rather than dispatch, however, the
mechanism to achieve reductions in unoccupied time would be
different for taxis than TNCs. The report discusses an
approach of allocating yellow cabs a set amount of time that
they can operate in the CBD during the business day.
A policy to reduce unoccupied time between trips would need
to balance the benefits of reducing the number of vacant
vehicles in congested traffic with the goal of maintaining good
availability of TNC and taxi service when customers want a
ride. Both unoccupied time of vehicles and waiting times for
customers should be monitored during implementation and
adjustments made as appropriate.
Reducing unoccupied time between trips for taxis and TNCs
can substantially reduce overall vehicle mileage in the CBD
and thus overall congestion levels. The report estimates that
overall vehicle mileage could be reduced by 7 percent to 11
percent from eliminating unnecessary unoccupied time
between trips.
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These reductions, if combined with congestion pricing and a
per-trip fee on taxi/TNC trips beginning in Manhattan (now
being discussed as part of a comprehensive program to reduce
congestion and raise money for public transit), would reverse
most of the drop in CBD speeds since 2010. A program with
these elements would reduce the number of vehicles in the
CBD by 20 percent or more, offsetting most if not all of the 23
percent drop in traffic speeds since 2010.
Finally, the report discusses implications of this research for
other cities, and for the anticipated arrival of autonomous
vehicles in the near future. Although New York City presents
unique circumstances compared with other large U.S. cities,
there are clear lessons to be gained from the New York
experience. Chief among these is the importance of the driver-
driven nature of the supply of TNC service in which overall
service hours are a product of decisions made by individual
TNC driver about where and when to work and how many
hours to drive. The dynamics underlying a driver-driven
supply of service is likely to lead to excessive time spent
between trips in cities across the country.
These findings also have significant implications for how fleets
of shared autonomous vehicles are likely to affect traffic
conditions in major cities as they are introduced in coming
years. It is anticipated that for many years to come, Uber, Lyft
and other ride service companies will have mixed fleets
composed of both autonomous vehicles and human-driven
vehicles. In the absence of policy intervention, expanding
mixed fleets will further balloon the number of vacant vehicles
occupied only by drivers waiting for their next trip request.
The results in this report thus heighten concerns about traffic
impacts from the arrival of autonomous vehicles. The results
also underscore the need for public policy to address traffic
impacts of both today's TNCs and tomorrow's fleets of mixed
human driven and autonomously driven ride services.
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Introduction
Manhattan traffic congestion is back in the news. In an
interview this August, Governor Andrew Cuomo said the
"time has come" to institute congestion pricing in the busiest
and most congested parts of Manhattan.1 In October, Cuomo
formed a 16-person panel to recommend steps to reduce
congestion in New York City and produce a dedicated funding
stream for the city's subway and bus system.2 Also this fall,
Mayor Bill de Blasio announced a series of steps to combat a 23
percent decline in Midtown traffic speeds since 2010, including
banning deliveries on certain cross-streets and stepping up
enforcement of parking and traffic rules.3
Manhattan's traffic problem imposes significant costs on both
motorists and on people who never get into a motor vehicle.
Most directly, congestion impedes the city's buses,
contributing to rapid declines in bus ridership over the last
four years, as well as other motor vehicles. Congestion also
increases the cost of freight movement, goods delivery and
provision of on-site services ranging from construction of new
commercial buildings to home repairs. These costs are passed
on to consumers whether or not they personally have an
automobile.
The drop in CBD speeds since 2010 is attributable to a variety
of factors. These include rapid employment and population
growth, increasing tourism and construction activity, and the
rise of on-line shopping and growth in package deliveries, to
name a few. Growth in all of these areas generates increased
pressures on the unchanging amount of Manhattan street
space.
Another important factor is the rapid expansion of Uber, Lyft
and other "Transportation Network Companies" (TNCs) in
New York City. There are now over 68,000 licensed TNC
vehicles in the five boroughs. A previous Schaller Consulting
study found that from 2013 to 2016, TNC growth added 600
million miles of travel to city streets. The study estimated that
over half of citywide growth in mileage occurred in Manhattan
and western Brooklyn and Queens.4
This report builds on the previous study with a more fine-
grained analysis that focuses on daytime traffic impacts in the
Manhattan CBD -- where congestion is most acute. The
analysis utilizes newly available data on TNC trips that show
where passengers were dropped off as well as where trips
began. Inclusion of trip destination makes possible a far more
detailed analysis of traffic conditions in the Manhattan CBD
than has been possible previously. The analysis isolates the
impact of TNC growth in the Manhattan CBD focusing on
weekdays during the business day and in particular the
afternoon peak, the most congested parts of the day.
Results show the net effects of several distinct trends. These
include a continuing decline in yellow cab ridership and rapid
growth in TNC ridership, the net result being increased overall
trip-making by TNCs and taxis. This growth, combined with a
trend toward longer trips (measured by mileage) and lower
vehicle utilization, has led to rapid growth in vehicle miles of
travel in the Manhattan CBD by the taxi/TNC sector.
Compounded by slower traffic speeds, the number of taxi and
TNC vehicles operating on CBD streets has increased even
more rapidly than trips or mileage.
As trips and mileage have increased, the taxi/TNC sector has
contributed to the worsening of congestion in the Manhattan
CBD. It is difficult to quantify how much of the problem is
attributable to these vehicles, however, due to lack of data on
mileage by other types of vehicles (e.g., commercial vehicles,
personal autos, etc.).
In addition to documenting growth in trips, vehicle mileage
and vehicle hours of taxi and TNC drivers, the analysis also
points to ways that these vehicles could help fix the congestion
problem. The most promising avenue is to reduce the
unoccupied time and mileage of taxis and TNCs. Over one-
thirds of drivers' time in the CBD is spent unoccupied between
passenger trips. The report discusses methods of reducing this
unoccupied time and estimates the potential benefit to traffic
speeds.
The report also discusses implications of this analysis for other
cities. Of most note is the way that driver-driven supply of
TNC services creates a dynamic that appears to make highly
likely that dense, congested downtown and entertainment
districts have more waiting vehicles than are needed for
prompt pick-ups. The recommendations to reduce this
unnecessary time between trips thus may be applicable to
other U.S. cities, although further analysis is needed to confirm
this.
There are also implications for the anticipated arrival of shared
autonomous vehicles, particularly in the long transition period
during which TNC fleets have a mix of autonomous operations
and human drivers. These implications are also discussed in
the report.
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1. Methodology
Findings in this report utilize extensive trip data on yellow cab
and TNC operations available from the New York City Taxi
and Limousine Commission (TLC). TLC rules require that taxi
owners and TNCs submit these data to TLC on a regular basis.
The main datasets used in this report are posted periodically
on TLC's website, while additional data was obtained through
Freedom of Information requests, as described below. The
report also uses monthly vehicle speeds in Manhattan
provided by the New York City Department of Transportation
which DOT compiles from the taxi trip data.
Starting in June 2017, TNC trip files include information on trip
destinations as well as trip origins. Having both origin and
destination information makes possible a fine-grained analysis
of travel patterns in the most congested parts of Manhattan.
The results represent the most in-depth examination of TNC
impacts in the core of a major American city, with public
policy implications for both New York City and other major
cities. This section describes source data and how they are
used in this report.
TNC and taxi trip data files posted on TLC's website include
start and end times of each trip and origin and destination
(using geographic zones). Taxi files also include distance, fare
and the number of passengers. Until recently, trip files for
TNCs contained the origin time and zone but not destination
time or zone. Starting with data for June 2017, however, under
a new TLC rule, destination times and zones are also included.
This report presents results from these files for June 2017 for
yellow cabs and TNCs and June 2013 for yellow cabs. Since
there were very few TNC trips in 2013,5 the period 2013 to 2017
shows the changes in the for-hire landscape produced by the
emergence and rapid growth of TNCs.
These public files are supplemented with trip data obtained
through Freedom of Information Law (FOIL) requests. These
include taxi trip data which show information for the previous
trip (start and end times and origin and destination zones) for
selected weeks in March/April 2013 and April 2017. Because
information about the previous trip is indicated, the
unoccupied time between trips (i.e., from drop-off of one
passenger to pick-up of the next passenger) can be calculated.
Also through a FOIL request, trip data showing previous-trip
information was obtained for Uber trips for the first half of
2016.6 This dataset was used to calculate unoccupied time
between trips for typical TNC operations.
Results from these two datasets were integrated with trip
volumes for June 2013 and June 2017. For taxis, the percentage
of "live time" (time with passengers) from the March/April
data was applied to June trip volumes. Using this spring data
with June data is not expected to distort the results because
"live time" was virtually unchanged between the spring and
June months of 2013 and 2017, according to monthly data
available on the TLC website.
For TNCs, the question might be raised whether unoccupied
time between trips changed between the first half of 2016 and
June 2017 in light of rapid growth from 2016 to 2017.
Unoccupied time appears to be quite stable over time,
however. A recent report co-authored by economists from
Uber and New York University found that, absent a change in
fares, vehicle utilization is consistent over time in major Uber
markets.7 Specific to New York City, trip duration and time
between trips was unchanged for CBD trips from February to
June 2016, even as trip volumes increased rapidly each month.
Further evidence that unoccupied time between trips tends to
be constant over time is the fact that trip volumes and the
number of TNC vehicles grew at the same rate between spring
2016 and June 2017. For utilization rates to change, trip
volumes would need to change at a different rate than the
number of vehicles (assuming that vehicles mileage is constant
over time, which appears to be the case based on TNC
odometer readings).
Findings in this report are based on analysis of these datasets
which include over 30 million trip records. The analysis
focuses on trips that begin and/or end in the Manhattan
Central Business District (CBD), defined as the area from 60th
Street to the Battery.
Results focus on several key metrics: trip volumes, time spent
with and without passengers, vehicle speeds, and vehicle
mileage. Trip volumes, trip durations and unoccupied time
between trips are calculated directly from the datasets as
described above. The calculation of time between trips takes
into account likely rest and meal breaks. Only the portion of
time that was likely spent waiting for or looking for the next
trip is included in the results. Maximum unoccupied time
between trips was assumed to be 30 minutes, and in most
EMPTY SEATS, FULL STREETS 5
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cases, is much shorter. Time between trips that exceed 30
minutes is assumed to be used for meal and other breaks.
Vehicle speeds are calculated based on taxi trip distance and
duration. (Distances are not available for TNC trips.) Because
CBD speeds vary from month to month, average CBD speeds
derived from June trips are benchmarked so that the change
from 2013 to 2017 matches the change in speeds for the first six
months in each year.
Mileage is based on speeds calculated from the taxi trip files
(based on distance and duration for each trip) and trip
duration. Trip distances are not captured for TNC trips nor for
unoccupied time by either TNCs or yellow cabs. These speeds
are likely to be similar to the speed that yellow cabs travel with
passengers, given that cab and TNC drivers are operating in
the same traffic conditions throughout the day, and are used
for estimating total mileage by these vehicles.
This report focuses on trips, time and mileage for trips that
start and/or end in the Manhattan CBD. For trips that enter or
leave the CBD, an estimate was made of the time and mileage
that was inside the CBD, using as a guideline trip distances
and duration for trips that ended just inside and outside the
CBD.
The analysis incorporates rates of "pooled" trips in which two
or more passengers, traveling independently, share a vehicle
for at least part of the trip. "Pooling" effectively reduces total
time with passengers. Pooling also eliminates unoccupied time
between trips for the second and any subsequent passengers
who join during the ride. TNC trip data obtained through
FOIL for June 2017 was used for this purpose.
This report uses the following terms to refer to the main
industry sectors:
Yellow cabs are licensed vehicles authorized to pick up
street hails throughout the city. The number of yellow
cabs, which has been regulated since the 1930s, is
currently 13,587.
Transportation Network Companies (TNC) are app-based
ride services, sometimes also called rideshare services.
Four TNC companies are currently operating in New
York City: Uber, Lyft, Via and Juno.
Uber, Lyft and Juno primarily provide exclusive-ride
services, with relatively low levels of pooled or shared
rides. Via primarily operates pooled services, with
passengers entering and exiting the vehicle during a
sequence of several customer trips.
5,446 Green cabs
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2. Findings
This section presents key results for TNC and yellow cab
activity for weekdays in the Manhattan CBD, when traffic is
most congested. Results are reported for passenger trips,
vehicle speeds and mileage and hour that taxis and TNCs
spend in the CBD in June 2013 and June 2017, and net increases
for combined taxi/TNC operations.
Results show how overall taxi/TNC trip growth has combined
with slower speeds and somewhat longer trips overall to
produce substantial increases in mileage traveled and in the
number of taxi/TNC vehicles in CBD traffic.
Results are presented first for average weekdays (24 hours)
and then by hour of the day.
Overall results (average June weekdays)
1. Passenger trips increased by 15 percent from 2013 to 2017.
From 2013 to 2017, decreases in taxi trips that began and/or
ended in the Manhattan Central Business District (CBD) were
more than offset by the growth of TNC trips. Taxi trips
declined from 378,000 to 250,000 on an average weekday (a
decline of 128,000 trips per day), while TNC trips increased
from virtually none to 202,000 trips per day. The combined
change in taxi/TNC trips, after adjusting for TNC trips being
dispatched to black cars vehicles and a small number of TNC
trips in 2013 (see Methodology), was 56,000 trips per day, an
increase of 15 percent. See Figure 1 on the next page.
2. Vehicle miles increased by 36 percent, reflecting trip growth, a trend toward longer trips and lower utilization rate.
In 2013, yellow cabs operated 1.05 million miles in the CBD on
the average weekday. Two-thirds of these miles were to
transport passengers and about one-third involved cruising for
the next fare.
Yellow cab mileage declined to 696,000 miles on an average
weekday in 2017; the utilization rate (mileage with passenger
as a percentage of total mileage) dropped slightly to 65 percent.
TNCs operated 802,000 miles in 2017 with a utilization rate of
60 percent.
Combining yellow cab and TNC mileage (with the adjustment
for black car dispatches and a small amount of 2013 TNC trips)
produces an increase of 378,000 miles per day. Combined
taxi/TNC mileage in the CBD thus increased 36 percent from
2013 to 2017. See Figure 2.
TNC trips are generally somewhat longer in mileage than taxi
trips. Due to longer trips and lower utilization, overall
mileage increased more rapidly than trip volumes.
3. Traffic speeds declined 15 percent overall, and by 18 percent during the day.
Traffic speeds are measured directly for yellow cab passenger
trips, which show both duration and distance. June data are
benchmarked to results for the first six months of 2013 and
2017 due to month-to-month fluctuations in traffic speeds, to
accurately reflect the decline in speeds over this period.
The overall decline in speeds from 2013 to 2017 was 15 percent.
Focusing on the period from 8 a.m. to 7 p.m. when traffic
speeds are much lower and the focus of congestion concerns,
the decline was 18 percent, bringing average June speeds down
to 6.8 mph from 8.2 mph in 2013.
4. The number of taxi/TNC vehicles in the CBD increased by 59 percent from 2013 to 2017.
Growth in the number of taxi/TNC vehicles in the CBD is
affected by increased trip volumes, longer trips and slower
speeds. In 2013, yellow cabs spent a total of 103,000 hours in
the CBD over the course of the day. In 2017, taxi vehicle hours
dropped to 81,000 while TNCs added 92,000 vehicle hours per
day. After making the adjustments described above, there was
a net increase of 62,000 taxi/TNC vehicle hours in the CBD.
This is a 59 percent increase from 2013. See Figure 3.
Dividing vehicle hours by the number of hours in a given time
period translates to the number of vehicles in the CBD at any
given time. Setting aside overnight hours, there were an
average of 9,100 taxis or TNCs in the CBD weekdays between 8
a.m. and midnight in June 2017.
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Figure 1. Taxi and TNC trips in the Manhattan CBD, 2013-17 (15% increase)
Figure 2. Taxi and TNC total mileage in the Manhattan CBD, 2013-17 (36% increase)
Figure 3. Taxi and TNC vehicle hours in the Manhattan CBD, 2013-17 (59% increase)
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Figure 4. Taxi and TNC occupied vehicle hours (with passengers) in the Manhattan CBD, 2013-17 (48% increase)
Figure 5. Taxi and TNC unoccupied vehicle hours (between passengers) in the Manhattan CBD, 2013-17 (81% increase)
*Black car adjustment accounts for trips dispatched by TNCs to black cars. These trips appear to be offset by declines in black car trips over this period, and thus are not counted toward increases in combined taxi/TNC trip volumes, mileage and time in the CBD. The adjustment for the small number of Uber trips in 2013 is also included in this figure (1% of the taxi figure).
Source: TLC trip files; see Methodology. Data are for trips that start and/or end in the Manhattan CBD, defined as 60 Street to the Battery, river to river. Data are for weekdays in June of each year.
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5. Taxi and TNC vehicle hours spent transporting passengers increased by 48 percent from 2013 to 2017.
From 2013 to 2017, the amount of time that yellow cabs spent
transporting passengers declined from 69,000 hours on an
average weekday to 53,000 hours. TNCs more than made up
the difference, with 55,000 hours with passengers in the CBD.
After making the above-mentioned adjustments, combined
taxi/TNC vehicle hours with passengers increased by 34,000,
an increase of 48 percent from 2013. See Figure 4.
6. Unoccupied taxi/TNC vehicle hours grew by 81 percent from 2013 to 2017.
While hours spent transporting passengers showed a large
increase, unoccupied hours grew even more quickly. Taxis
spent 34,000 unoccupied hours in the CBD in 2013, decreasing
to 29,000 in 2017. Meanwhile, TNCs added 37,000 unoccupied
vehicle hours. After making the adjustments described above,
the net increase was 34,000 vehicle hours in the CBD on
weekdays. This is an increase of 81 percent from 2013. See
Figure 5.
Unoccupied vehicle hours grew more rapidly than occupied
vehicle hours due to lower utilization rates. While yellow cabs
were occupied with passengers 67 percent of the time in 2013,
the utilization rate for combined taxi/TNC operations dropped
to 62 percent in 2017.
Results by time of day
1. Passenger trips increased most rapidly in the late afternoon and early evening, with the least rapid growth occurring in the morning peak and midday.
Trip volumes grew most rapidly during the afternoon peak
period, with increases of about 50 percent from 4 p.m. to 6 p.m.
(See Figure 6.) The faster growth in the afternoon peak is
largely due to smaller declines in yellow cab trips during this
period, compared with other times of the day. In addition,
TNC trip volumes ramp up starting in the mid-afternoon,
adding to growth in the afternoon peak.
Growth was also relatively rapid in the "shoulder" hours of 3-4
p.m. and 6-7 p.m.
2. Vehicle miles shows the same time-of-day pattern, with mileage increasing by 46 percent or more from 3 p.m. to 7 p.m.
As mentioned earlier, TNC trips generally cover more mileage
than taxi trips. As a result, the addition of TNCs has led to
more rapid growth of vehicle miles traveled than trips. As
with trips, mileage increased most rapidly in the afternoon
peak period. Total mileage grew by 68 percent or more from 4
p.m. to 6 p.m. and about 50 percent in the adjoining "shoulder"
hours.
3. Speeds declined about the same throughout the day, with slightly larger reductions during the afternoon peak.
Time-of-day variation for the change in taxi speeds are much
less pronounced than for trips and mileage. Speeds declined
19 percent from 3 p.m. to 7 p.m., compared with 17 percent
earlier in the day and 16 percent in the evening.
4. The total number of taxi/TNC vehicles more than doubled between 4 p.m. and 6 p.m., and increased by 50 percent or more every hour from 1 p.m. to 8 p.m.
Following from the larger increases in trips and mileage, the
number of taxi/TNC vehicles in the CBD more than doubled
from 4 p.m. to 6 p.m., and by at least 50 percent every hour
between 1 p.m. and 8 p.m.
5. The number of occupied taxi/TNC vehicles nearly doubled, and the number of unoccupied vehicles nearly tripled, between 4 p.m. and 6 p.m.
Because of the decline in utilization (percentage of time with
passengers), the most rapid increases were in the number of
unoccupied vehicles, particularly in the late afternoon when
they nearly tripled. There are over 3,200 unoccupied taxi/TNC
vehicles in the CBD from 5 p.m. until midnight in the CBD.
Figure 7 shows the number of taxi/TNC vehicles in the CBD
hourly from 6 a.m. to midnight. The number of vehicles peaks
at over 10,000 vehicles from 4 p.m. to 6 p.m., and remains
above 9,000 vehicles until 11 p.m.
* * *
As evident from the hourly bar charts, there are large time-of-
day differences in the growth of trips, mileage and the number
of taxis and TNCs in the CBD. Figure 8 shows the changes in
these metrics from 2013 to 2017 for two time periods, weekday
daytime hours between 8 a.m. and 7 p.m. and in the afternoon
period between 3 p.m. and 7 p.m.
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Figure 6. Hourly Change in Trips, Vehicle Miles, Speeds and Vehicles in the CBD, 2013 to 2017
Change in trips (with passengers)
Change in total mileage (with passengers and unoccupied)
Change in speeds
Change in total hours (with passengers and unoccupied)*
Change in occupied hours (time with passengers)*
Change in unoccupied hours (time between trips)*
*Hours translate directly to the number of vehicles in the CBD on an hourly basis (i.e., one vehicle hour is equivalent to one vehicle present in the CBD during that hour period).
Source: TLC trip files; see Methodology. Data are for trips that start and/or end in the Manhattan CBD, defined as 60 Street to the Battery, river to river. Data represent changes between June 2013 and June 2017 ( weekdays).
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Figure 7. Number of taxi/TNC Vehicles in the CBD, by hour, weekdays June 2017
Figure 8. Change in Trips, Vehicle Miles, Speeds and Vehicles in the CBD, 2013 to 2017 Selected Time Periods
Source: TLC trip files; see Methodology. Data are for trips that start and/or end in the Manhattan CBD, defined as 60 Street to the Battery, river to river. Data represent changes between June 2013 and June 2017 ( weekdays). See the Appendix for further detail.
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Traffic impacts
These large increases in the number of vehicles (both occupied
and unoccupied) in the CBD clearly have a very significant
impact on CBD traffic flow. The growth in taxi/TNC vehicles
is even more remarkable given that traffic counts at avenues
crossing 60th Street and the East River crossings show steady
declines in the number of vehicles entering the CBD. As a
result of these two trends -- more taxis/TNC vehicles but an
overall drop in vehicles entering the CBD -- taxis/TNC
vehicles have become a very large part of overall traffic.
Estimates for the 60th Street cordon indicate that during
daytime hours, taxis and TNCs likely comprise 50 percent or
more of total vehicles traveling north or south.
It is sometimes argued that traffic impacts from trip data are
overstated because TNC drivers may not actually be driving
around between trips, but waiting at the curb for their next
trip. This is an important question worth considering.
However, available data indicate otherwise.
First, an analysis of odometer readings taken from TNC
vehicles at TLC inspection show that TNCs have a passenger in
them for approximately 55 percent of overall miles driven (after
accounting for personal use of the vehicle). Likewise, trip data
show that about 55 percent of time is spent with passengers.
(Both sets of data are citywide, 24/7.) In other words, for
every 100 miles that a TNC vehicle is driven, about 45 miles are
unoccupied. For every 10 hours of operation, about 4.5 hours
are unoccupied. One can readily infer from these data that
unoccupied time (45 percent) is spend in motion, since 45
percent of the mileage is unoccupied mileage.
Second, analysis of trip patterns shows that unoccupied
vehicles crossing the 60th Street screenline are consistent with
about one-third of TNC mileage in the CBD being unoccupied,
consistent with the other results from this analysis.
Finally, as discussed in the next section, a main opportunity for
TNCs to reduce congestion lies in reducing the deadheading of
TNC drivers who return to the CBD after dropping off
passengers elsewhere. This deadheading clearly involves time
and mileage in traffic.
The large increase taxi/TNC vehicle hours is thus an important
source of slow traffic conditions in the Manhattan CBD.
Increases in unoccupied time are worth focusing on since the
increased time and mileage spent between trips does not
contribute to the mobility needs of New Yorkers while they do
contribute to congestion. Reducing unoccupied time presents
an opportunity to reduce Manhattan traffic congestion and
improve both mobility (through less congested traffic) as well
as driver incomes. This opportunity will be explored in more
detail in the next section of this report.
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2. Policy Options
Over the years, City officials have sought to use a range of
policy and operational tools to speed up traffic in the
Manhattan CBD. Technology-based remedies such as the
City's real-time adaptive signal control systems are effective
and widely supported by the public. Strategies involving
allocation of street space, such as banning curbside parking
during certain hours, turn restrictions and prohibiting
"blocking the box" are generally welcomed by the public and
are effective in improving traffic conditions if motorists
comply with the rules. On the other hand, strategies such as
congestion pricing that seek to discourage motor vehicle
travel are highly controversial because motorists who would
pay the congestion charge may not believe that the benefits
to them are commensurate with what they would have to
pay.
It is worth bearing these experiences in mind when
considering policy options to address increases in taxi/TNC
trips, mileage and vehicles during the day in congested parts
of Manhattan. Previous attempts to limit TNC operation
have met with strong resistance. However, reducing
unnecessary time and mileage spent in the CBD between
passenger trips could be very appealing. The fact that the
unoccupied time between passenger trips is the fastest-
growing aspect of for-hire operations in the CBD makes
focusing on it particularly attractive.
Currently, over one-third of taxi/TNC vehicles in the CBD
are unoccupied at any given time during weekdays. The
presence of some vacant vehicles is essential to service --
some drivers need to be available for the next passengers
looking for a ride. The large increase in vacant taxi/TNC
vehicles over the last four years raises the question,
however, of whether the number of taxis and TNCs available
for the next customers is currently excessive.
This question can be addressed by examining how TNC and
taxi drivers spend their time. TNCs need to be considered
separately from taxis, due to important differences between
TNC pre-arranged dispatch operations and yellow cab street
hail operations.
Reducing unoccupied time between trips by TNCs
TNC trips that begin in the CBD take an average of 24
minutes for weekday trips. TNC drivers also spend an
average of 11 minutes between dropping off one passenger
and the next pick-up. Unoccupied time includes the time
needed to drive to the next customer's location for pick up
and time spending waiting to be dispatched. Pings on TNC
APIs show that the estimated wait time for customers
requesting a TNC ride is three to four minutes on average in
the CBD throughout the day. Thus, of the 11 minutes
(average) time spent between trips, a few minutes are
required to drive to the next customer and the balance of the
11 minutes is spent waiting for a trip request. The
proliferation of waiting drivers is easily seen on the Uber
and Lyft apps, which show numerous drivers available for
dispatch clustered near any location randomly selected in
the CBD, particularly in Midtown.
If there were fewer drivers waiting for their next trip
request, passengers would still receive prompt service, as
shown in other parts of the city where pickups are quick
even though available vehicles are more spread out than is
the case in the CBD. But customers would travel faster to
their destination since there would be fewer unoccupied
TNC vehicles in the traffic mix.
The opportunity to reduce the number of TNC vehicles in
the CBD lies in the time that drivers wait for dispatch.
Typically, drivers spend 3-11 minutes between trips. (See
Figure 8 on the next page.) This includes the time spent
driving to the pickup location (typically 2-4 minutes). The
balance of time is spent waiting for a trip request.
Notably, a significant amount of the time between trips
originates with drivers experiencing well over 11 minutes
between trips. These most likely arise when drivers drop-off
outside the CBD and then drive back into the CBD in search
of their next fare.
How might unoccupied time between trips be reduced or
eliminated? The answer becomes clear with an explanation
of why there is so much unoccupied time between trips in
the first place.
Unlike transportation services such as public transit and
intercity buses, the amount of service available to potential
customers is decided by drivers, not by the company
operating the service. Because the main reason to drive a
cab or TNC vehicle is to make money, the number of drivers
on the road is highly responsive to potential earnings.
Drivers work where they can make relatively good money
EMPTY SEATS, FULL STREETS 14
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Figure 9. Unoccupied Time Between Trips
Source: 2016 Uber trip data.
and avoid areas where there is less money to be made. Not
only where drivers work but also how much they work is
highly responsive to potential revenues. Drivers spread out
across the city and across the day and week to produce
similar average earnings per hour geographically and
temporally.
This dynamic can be seen very clearly in the TNC data. One
might expect that drivers in Manhattan are much busier than
drivers in the other boroughs, since the density of demand is
so much higher in Manhattan. But this is not the case.
Drivers spend about the same amount of unoccupied time
between trips in Brooklyn and Queens, for example, as in the
Manhattan CBD. Trip durations are also about the same. As
a result, although there is much variation from driver to
driver, average hourly fare revenue are similar no matter
where in the city drivers choose to work. In economic terms,
this balancing creates a market clearing equilibrium for
driver wages across geographies and time of day.
This dynamic is also seen within the Manhattan CBD.
Figure 9 shows the distribution of unoccupied minutes
between trips for trips originating in the Manhattan CBD in
selected time periods. Notably, the distribution of wait
times is almost identical whether considering areas with
high trip volumes and pick-ups exceeding drop-offs
(Midtown in the afternoon) and situations with lower trip
volumes and drop-offs exceeding pick-ups (the whole CBD
in the morning). Thus, Manhattan shows the same market
clearing equilibrium as the city as a whole. Through
individual, largely independent decision-making, drivers are
remarkably able to equalize trip flows across vastly different
trip volumes and travel patterns.
Several approaches can be considered to reduce unoccupied
time between trips. Advantages and disadvantages to each
approach need to be considered. The purpose of this report
is to discuss potential approaches and their pros and cons,
and thus provide a basis for public discussion of policy
options.
One approach currently under discussion is for the State or
City to levy a tax or fee on TNC trips that traverse CBD
streets. The charge might be in the range of $2 to $5. There
is much precedent for taxes or fees of this kind. TNC
customers already pay sales tax on rides, with revenues
going to the City and State. Taxi passengers pay a 50-cent
surcharge that goes to the MTA. Trip fees and taxes can
generate a substantial flow of revenue. A $3 fee on every
TNC and taxi ride that begins in the Manhattan core would
yield about $475 million annually.8 (This estimate is based
on a charge that applies to all trips, 24/7, starting in
Manhattan south of East 96 Street and West 110 Street.)
Per-trip fees are effective in raising revenue, but not so
effective in combating congestion. TNC and taxi riders are
generally well-off and have chosen, particularly for trips in
the CBD, to take a TNC or cab instead of using public transit,
biking or walking. They are thus relatively insensitive to
price increases. Raising the cost of trips through a trip fee
will do relatively little to reduce trip volumes or TNC (or
taxi) mileage in the CBD.
Although there do not appear to be studies specific to TNCs,
studies of taxi fares have found that a 10 percent increase in
the fare is expected to produce a 2 percent to 2.5 percent
reduction in ridership.9 Applying these figures to TNC trips,
a $3 fee would be expected to reduce TNC trip volumes (and
mileage) by 3-4 percent. While not an inconsequential
figure, a per-trip fee would not produce much congestion
relief in the CBD.
Another approach is to more directly target unoccupied
time. The City or State could require that TNC companies
reduce excessive unoccupied time by the vehicles dispatched
by them. "Excessive" could be defined as the time greater
than needed for driving to the pick-up location. As the
current average appears to be 3-4 minutes, unoccupied time
over four minutes between trips would be subject to a
penalty (most likely financial). The objective of the penalty
would be to strongly incentivize TNC companies to
minimize the unoccupied time of their drivers.
The advantage of this approach is that it leaves up to the
companies the best way to achieve the objective of reduced
unoccupied time. They have the technology to monitor
drivers' time and trip patterns and adjust dispatch
procedures to minimize unoccupied time between trips.
In fact, TNCs have already implemented dispatch
procedures to minimize unoccupied time between trips at
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airports, with the same objective as being discussed here,
namely, to reduce congestion.
At airports across the country, including JFK and LaGuardia
airports, Uber and Lyft employ what they call "rematch."
The companies' dispatch systems offer trips to drivers at
airport terminals just as they drop off an arriving passenger.
This avoids the driver dead-heading to the waiting area
several miles away while another driver goes from the
waiting area to the terminal. Rematch makes for fewer
unoccupied miles on airport roadways, fewer TNC vehicles
at terminal frontage, and quicker pickups for passengers.
TNCs could apply rematch to Manhattan drop-offs. Using
rematch, TNC drivers could be offered a new trip just as
they complete trips with CBD destinations. This would
expand current TNC practices, since they already have a
"pre-dispatch" procedure to book the next trip with drivers
just as they finish up their current trip. For pre-dispatched
trips, drivers spend virtually no time waiting for their next
trip request.
During most of the day, pick-ups exceed drop-offs, so there
would be a need for a few TNC drivers to "deadhead" into
the CBD without a passenger. Here, the balancing dynamic
discussed earlier would appear to work in favor of
minimizing unoccupied time. If TNCs prioritize drivers
making drop-offs in the CBD, drivers deadheading into the
CBD would have to wait until no other drivers were nearby
a requesting passenger before getting a trip. Only a few
drivers would have the incentive to deadhead into the CBD.
While this may sound unlikely to work in practice, in fact, it
already happens every hour of every day. Consider a driver
who picks up in Midtown and takes a passenger to Long
Island City. After dropping off the passenger, the driver can
decide to stay in Queens, where he can expect to wait about
5-10 minutes for his next trip. Or he can drive back to
Manhattan and wait there. Currently, some drivers
deadhead into Manhattan, some stay in Long Island City,
and some go elsewhere such as to LaGuardia airport. The
data show drivers make a mix of decisions that have the net
effect of equalizing unoccupied time across the city.
With the changes in dispatch discussed above, a driver
making a drop-off in Long Island City would have the same
choices he has now. Just as happens today, if "too many"
drivers choose to go back into Manhattan, wait times there
rise and some drivers make a different choice the next time.
This is the way unoccupied times equalize across the city.
What does change in the new situation is that the threshold
of "too many" falls. With drivers making drop-offs receiving
priority for the next customer, drivers considering dead-
heading into the CBD have less incentive to do so. As a
result, there become fewer unoccupied vehicles in the CBD.
This dynamic appears to be the most likely outcome of
applying rematch to Manhattan drop-offs. But this expected
outcome needs to be tested in practice. Just as they began
with tests of the rematch system at airports, TNCs can test
applying rematch to CBD drivers, monitor the results and
make adjustments as may be necessary.
Rematch is most beneficial to reducing congestion in the
afternoon, when pick-ups exceed drop-offs and so virtually
all drivers completing a trip should be able to serve their
next trip nearby. In the morning, the number of drop-offs
exceeds the number of pick-ups in the CBD as people come
to work, but fewer are leaving the Manhattan core. As a
result, there are not enough pick-ups for all drivers to
quickly transition from drop-off to pick-up. Pre-dispatch
would still reduce the number of unoccupied TNC vehicles,
however, discouraging drivers from deadheading into the
CBD as some do now.
The role for the City or State should be mandating reduced
time between trips and monitoring compliance.
Government should mandate the outcome it wants (reduced
unoccupied vehicles), not the method of achieving it.
In testing rematch in the CBD, TNCs and regulatory
oversight agencies should watch for possible unintended
consequences. For example, do drivers resist taking
passengers outside the CBD given the new priority system?
The volume of TNC business throughout the city mitigates
against this, but it would need to be monitored and
corrective actions developed if necessary.
There is a degree of balancing involved in minimizing
unoccupied time between trips without inflating customer
waiting times. TNCs and regulators would need to
determine how best to achieve and maintain a balance that
serves the purposes of both good customer service and
reduced CBD congestion.
Reducing unnecessary time between trips for taxis
Cab drivers' trips are generally shorter than is the case with
TNCs, and the time between trips is also shorter. The
average cab trip that starts in the CBD lasts 16 minutes.
Unoccupied time between trips averages 8 minutes,
primarily cruising looking for a fare-paying customer.
Unoccupied time varies between 8 and 11 minutes over the
course of the day. This is substantially more than in 2013,
when drivers spent an average of 5 to 9 minutes between
trips, with lower figures during the afternoon rush hour.
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A reasonable target for unoccupied time can be inferred
from the 2013 data. During the evening rush hour, when
there were reports that cabs were hard to find,10 drivers
spent an average of 5.0 to 5.5 minutes between trips. At
other times during the day, they spent 6 minutes or more
between trips.
A reasonable target that would minimize unoccupied time
but retain good cab availability is probably 6 minutes, the
point at which cab service was reasonably easy to get in
2013. Further efficiencies would likely be possible if yellow
cabs also widely took trips via smartphone app, which TNC
experience shows creates the potential for somewhat lower
time between trips (e.g., as discussed above, 3-4 minutes on
average, from accepting a trip to pick-up).
The same concept of mandating reductions in unoccupied
time in the CBD can be applied to yellow cabs. However,
the mechanism needs to be different. Yellow cabs obtain
most of their business through street hails. Although some
have apps, cabs do not generally respond to dispatch
requests that are funneled through a central office.
Instead of working through central companies as with
TNCs, yellow cab time in the CBD could be regulated for
each taxicab. Each cab could be allocated a predetermined
number of hours that it could work in the CBD during
congested hours (e.g., 8 a.m. to 7 p.m.). Drivers could
choose when to work in the CBD and when to work
elsewhere or at other times. They would need to always
have some of their allocation unused so that they could take
a customer into the CBD after being picked up elsewhere.
As with TNCs, there would need to be penalties for going
over the allocation of CBD hours.
The technology for this system is largely in yellow cabs
already, since TLC receives trip data for each trip. The
system would need to be enhanced so that regulators could
audit the records to prevent cheating or evasion.
A system to allocate CBD hours could be phased in over a
period of months, with effectiveness monitored on an
ongoing basis. As with TNCs, the objective would be to
strike a balance between reducing unoccupied time and
maintaining good availability of taxi service.
Drivers would benefit from this system because they would
make more money while in the CBD than they do now, since
there would be less unoccupied time between trips.
It should be noted that daytime CBD work is a surprisingly
modest fraction of overall yellow cab activity. In June 2017,
only 27 percent of all taxi trips began in the CBD on
weekdays between 8 a.m. and 7 p.m. Evenings and week-
Table 1. Reductions of Taxi/TNC vehicles in the Manhattan CBD and vehicle mileage from reductions in unoccupied time between trips
Scenario 1 Scenario 2
Unoccupied time between trips
4 minutes 6 minutes
Change in Taxi/TNC vehicles in CBD
-19% -12%
Estimated reduction in vehicle mileage in CBD (all vehicles)
-11% -7%
Results are for weekdays, 8 a.m. to 7 p.m., based on June 2017 data.
ends account for the large majority of taxi trip origins. While
a significant change, the system discussed here would not
have a major impact on overall taxi operations, nor would it
subtract from industry revenues.
Traffic benefits
This analysis shows that there are opportunities to improve
traffic flow by reducing the pool of empty cabs and TNC
vehicles in the CBD. Furthermore, this can be done without
compromising how long it takes to get a ride. Table 1 shows
two scenarios that quantify the reduction in taxi/TNC
vehicles and the potential traffic benefit.
In the first scenario, TNC and yellow cab operations are
optimized and reduce time between trips to an average of
four minutes. TNC drivers get their next dispatch just as
they drop off the previous customer. Yellow cab operations
are optimized with a combination of street hail, app usage
and cab stands so that they match the efficiency of TNCs.
Under the current industry structure with cabs and TNCs
operating independently, four minutes between trips
represents a best-case scenario.
In the second scenario, time between trips is reduced to six
minutes, matching the figure seen for cabs in 2013 and
allowing TNCs a couple of minutes between drop-off and
acceptance of the next trip. This is probably a more realistic
scenario for implementation.
As shown in Table 1, total time that taxis and TNCs spend in
the CBD between 8 a.m. and 7 p.m. falls by 19 percent in
scenario 1, and by 12 percent in scenario 2.
Reductions in the number of taxi and TNC vehicles in the
CBD would help improve CBD traffic conditions. Taxis and
TNCs combined comprise 50 percent to as much as 75
percent of all traffic in the CBD, depending on time of day.
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Based on estimates of total traffic volumes, CBD traffic
(including all vehicles, not just taxis and TNCs) would
decline by 11 percent in scenario 1 and seven percent in
scenario 2 during daytime hours. Vehicle speeds would
likely increase by about the same percentage.
Daytime CBD speeds have declined by 23 percent since 2010.
This one step -- reducing unnecessary driving by taxis and
TNCs during the day in the CBD -- would reverse at least
one-third of that decline.
The projected improvements to CBD speeds is also
significant when compared with the impact of other
potential measures. The 2008 Bloomberg Administration
congestion pricing proposal, for example, projected that an
$8 fee on vehicles entering the CBD would reduce vehicle
mileage traveled (VMT) by 7 percent, with about the same
increase in traffic speeds.11
Combining these elements could reverse most if not all of
the decline in CBD speeds since 2010. A cordon congestion
pricing plan, with one-way tolls a bit over $5, would
potentially reduce traffic by more than the 7 percent
expected from the 2008 proposal which had a one-way $8
fee, with offsets for other tolls paid on the same day (e.g.,
Hudson River crossings). Reducing unoccupied time would
reduce overall traffic volumes by 7-11 percent, and a per-trip
fee by about 2 percent. Adding these together yields close to
a 20 percent reduction in traffic volumes.
Other sources of unnecessary driving
Reducing the unnecessary time between trips that taxi and
TNC drivers spend in the CBD is one way to improve traffic
conditions in the Manhattan CBD, but not the only one.
There are other opportunities that relate to this group of
vehicles as well as other frequent users of Manhattan streets.
Policies to reduce unnecessary time between trips should be
a first step toward addressing these other opportunities, two
of which can be mentioned here.
First, the TNC trip data show that TNCs take longer to go
between a given pair of origin and destination zones than do
yellow cabs. Differences of three to four minutes are seen
throughout origin/destination zone pairs for daytime trips
in the CBD. It seems likely that TNC trips take longer than
taxi trips because TNC drivers generally pick up customers
at their doorstep while taxi users often walk to the nearest
avenue. TNC drivers may need to go around the block to
head toward the destination. The extra time for TNC trips
would thus be the time required to go around the block,
likely waiting at a red light after each turn.
TNCs have been experimenting with having customers walk
a block or so to a designated pickup location for "pooled"
(shared-ride) trips. Uber is also experimenting with advising
UberX passengers that walking to a nearby corner will result
in a faster overall trip. Clearly, the technology is available to
combine walking and being driven, to the benefit of
passengers, drivers and the efficiency of the street system.
Widespread implementation of this approach should be
considered.
Moving beyond TNCs and taxis, there are obvious
inefficiencies in the use of street space by trucks and
commercial vehicles. These vehicles often double-park and
may also "block the box" at intersections. Delivery trucks
can also be seen using loading zones for the entire day
despite 3-hour time limits.
These inefficient uses of scarce street space are analogous to
the unnecessary time between trips by taxis and TNCs. The
policy objective would also be analogous -- to create
efficiencies that serve to improve traffic conditions and, at
the same time, benefit commercial drivers and their
companies. The means would need to be derived from
careful analysis of the source of inefficiencies and
development of remedial steps.
Implications for Other Cities
In considering the implications for other cities, it is
important to recognize that New York is in many respects
quite different than other large U.S. cities. New York's
density (population and employment) and extensive public
transportation system need to be taken into account in
thinking about how its experience translates elsewhere.
While bearing in mind differences in size and density, the
findings in this report are relevant to other large American
cities.
Most centrally, the results in this report show the importance
of the driver-driven nature of the supply of TNC service. In
this respect, New York is no different than any other city,
suburban or rural area. Drivers choose how much to work
and where and when to drive. (TNCs tout this flexibility in
recruiting drivers.) One sees in the data that at times and
places that customers are plentiful, more drivers go on the
road. Conversely, when and where customer demand
drops, fewer drivers log onto the TNC app.
Because drivers are so responsive to customer demand,
drivers make about the same amount of money in
neighborhoods with low trip volumes as in neighborhoods
of high trip volumes. This is seen in New York, comparing
Brooklyn and Queens, for example, with Manhattan. The
same dynamic is seen across the country. A recent paper by
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economists at Uber and New York University, using trip
data from major Uber markets, found that absent a change in
the rate of fare, Uber vehicle utilization (the proportion of
time drivers have passengers) has remained highly
consistent over time, even as the company has grown
rapidly.12 Thus, consistency in driver fare revenues is seen
both across geographies and across time, despite large
variations in trip volumes.
This balancing dynamic has worked to the public's
advantage in important ways. TNC rides are available in
many suburban and rural areas, as well as in some city
neighborhoods that historically had deficiencies in taxi
service availability if they had cab service at all.
But in an urban context, this dynamic means that drivers
spend a considerable amount of time waiting for their next
trip request, as this report shows for the Manhattan CBD
and in fact, throughout the five boroughs.13 Since customer
wait times are short, drivers spend most of the time between
trips simply waiting for a trip request -- and clogging the
streets as they do so.
The larger and denser the city, the more time drivers spend
waiting for the next trip dispatch. Thus, extra TNC vehicles
are most likely to clog downtown office centers and
entertainment districts with a high demand for TNC rides,
compounding already existing congestion problems. This
outcome is seen in New York and is likely to be found in
cities across the country.
A second implication for other cities concerns opportunities
for more efficient use of scarce city street space. Traffic
management is often assumed to involve trade-offs between
competing users. It is easy to assume that to improve traffic
conditions, someone or something has to give -- drivers need
to be charged a fee, or traffic and parking enforcement
needs to ramp up, or trucks need to shift deliveries to off-
hours. The good news from this analysis is that while those
steps all have merit, there are also less painful opportunities
to make traffic flow better. Reducing excess time and
mileage spent by TNC drivers waiting for their next trip can
improve mobility for everyone, as well as increase driver
incomes.
Implications for Automated Vehicle Deployment
While TNCs have wrought major changes to how people
move around cities, the transformation spawned by TNCs is
likely to pale in comparison to the effects of autonomous
vehicles. After years of testing with a human at the wheel to
take over when needed, autonomous vehicles without a
human back-up are likely to arrive in cities surprisingly
soon. Google's Waymo unit recently announced the start of
autonomous vehicle testing in Phoenix with an employee in
the back seat instead of behind the wheel; General Motors
recently announced plans to launch fleets of fully
autonomous vehicles in dense urban areas in 2019.14
Autonomous vehicles will likely be used in ride service
fleets, whether Uber, Lyft or other companies established by
car manufacturers. The reason for this is cost. At the outset,
autonomous vehicles will be considerably more expensive
than conventional motor vehicles. To make the finances
pencil out, they will have to be used intensively, with as
many passengers paying as much in fares as possible.
Vehicle developers have thus focused on testing
autonomous vehicles in the demanding urban environments
such as San Francisco, with plans in the works for testing in
New York City as well.
While shared autonomous vehicles (SAVs) are likely to
arrive quite soon, it is anticipated that ride service fleets like
Uber and Lyft will continue to have human drivers for many
years to come. Companies will be loath to depend entirely
on a new technology until it is completely proven and
shown to overcome operating limitations such as how well
the sensing technology can "see" in heavy rainstorms and
how well it can handle snow that covers lane markings.
The traffic impacts of TNC growth will be magnified as TNC
fleets continue to expand and as they begin to add shared
autonomous vehicles. The market-clearing dynamic
discussed above means that drivers will continue to stream
into dense city centers, spending excessive time waiting for
their next trip, unless mitigation steps are taken. Moreover,
once the costs of operating shared autonomous vehicles
drops below the cost of human-driven TNC operations
(taking into account higher vehicle costs but the absence of a
driver in autonomous vehicles), fares are also likely to drop.
Declines in fares will spur further growth, with impacts on
both traffic volumes and transit ridership.
In the long run, SAVs can bring myriad benefits to cities.
These range from reduced traffic injuries and fatalities to
reducing the use of single-occupant vehicles, freeing parking
spaces for new housing and commercial buildings, and
increased use of electric vehicles.15
While recognizing those benefits, this report points to risks
in the long transition period that precedes a fully
autonomous future. These findings thus underscore the
important role for public policy in managing traffic impacts
as the day of shared autonomous fleets in its major urban
centers approaches.
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4. Conclusion
App-based ride services have established themselves as an
attractive and often-used transportation option in cities large
and small across the United States. Their services now rival
traditional public transportation in reach and ridership. TNC
patronage has grown to 75 percent of total bus ridership in
New York City, and approximately 65 percent of total bus
ridership nationally.16 Offering quick, reliable and comfortable
service, TNCs have built a broad base of frequent users in
major cities across the country.
While clearly beneficial to urban mobility on an individual
level, the growth of TNCs has raised a range of issues
concerning traffic, transportation and environmental impacts
as well as equity, particularly for lower-income persons and
people who use wheelchairs. While these concerns have been
discussed in a range of cities, there has been very little data to
develop a detailed understanding of impacts or form the basis
of a public policy response.
This report focuses on very fine-grained trip data available in
New York City, with the purpose of understanding TNC
impacts in the nation's largest and densest metropolis, and in
hopes of offering insight for other large American cities.
Findings indicate that relatively modest growth in overall
taxi/TNC trip-making (a 15 percent increase in the Manhattan
CBD over the last four years) translates into far larger growth
in miles driven and the number of taxi/TNC vehicles in the
CBD during the business day. The largest growth is seen from
4 p.m. to 6 p.m., during which the number of taxi/TNCs in the
Manhattan CBD more than doubled over the last four years. In
the late afternoon there are nearly 10,000 taxi/TNC vehicles in
the CBD; they comprise well over one-half of all traffic. These
vehicles contribute to what are now the slowest traffic speeds
(less than 7 mph during the day) on record in the Manhattan
CBD.
In the wake of growth in TNC trips, policy-makers are
presented with a dilemma. There is little appetite for limiting
TNC operations given their widely-enjoyed mobility benefits.
On the other hand, in highly congested urban environments
such as Manhattan, TNCs are contributing to very slow traffic
flow, at a cost both to their own customers and drivers as well
as everyone else on the road.
A per-trip fee on taxi and TNC trips, currently part of
discussions of potential congestion pricing solutions to the
Manhattan traffic problem, would raise substantial revenue
but only modestly reduce taxi/TNC vehicle mileage in
Manhattan. This report estimates that a $3 per-trip fee, for
example, would reduce taxi/TNC mileage by 3-4 percent while
at the same time generating $475 million per year.
A more promising approach is to focus on the unoccupied time
that taxis and TNCs spend between dropping off passengers at
the end of one trip and picking up passengers for their next
trip. Approaches to reducing unoccupied time are discussed in
the report, with the most promising approach being a mandate
on TNC companies and yellow cab owners to reduce time
spent in the CBD.
The report estimates that reducing unoccupied time and
mileage could reduce the number of taxi/TNC vehicles in the
Manhattan CBD by 12-19 percent. This would produce an
estimated 7-11 percent reduction in overall traffic in the CBD
on weekdays from 8 a.m. to 7 p.m., and likely a commensurate
increase in traffic speeds.
Together with congestion pricing, which was projected to
reduce CBD vehicle mileage by seven percent when considered
a decade ago, and a per-trip fee on taxi and TNC trips, most if
not all of the 23 percent decline in CBD speeds since 2010 could
be reversed.
Further development of a regulatory response to reduce
unoccupied vehicles should include TNC and yellow cab
companies and drivers who would be affected by a mandate to
reduce unoccupied time as well as public officials responsible
for adoption and implementation of such a policy.
This report also discusses the implications of the New York
City experience for other large U.S. cities and for the coming
advent of autonomous vehicles operating in shared fleets.
Results from New York show how the business model used by
TNCs affects traffic levels. The number of TNC vehicles on the
road at any given time and place is set by decisions made
individually by TNC drivers, each of whom decides where and
when and how much to work. This dynamic is highly
beneficial in making TNC service quick and generally reliable
in places that never had reliable taxi service, if there was any
cab service at all. But this dynamic also leads to an
unnecessarily large number of unoccupied TNC vehicles
throughout New York City, most notably, in congested areas of
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Manhattan. The same is likely to be true in other major U.S.
cities.
This dynamic will continue with introduction of autonomous
vehicles. Shared autonomous vehicle (SAV) services are
expected to include both autonomous vehicles and human-
driven vehicles. At the same time, reduced costs from
replacing drivers with autonomy in part of the fleet is likely to
reduce passenger fares and spur further growth in TNC trip
volumes. The end result is likely to be accelerated growth of
unoccupied TNCs in mixed fleets of human-driven and
autonomously operated TNC services.
Both the current continued growth of TNCs, and accelerated
rates of growth likely with autonomous vehicles, will call for
public policy responses. This report is intended to help inform
the development of effective policy responses that both take
full advantage of the coming changes to urban transportation,
and manage and mitigate the risks posed by continued
proliferation of motor vehicles in the nation's largest cities.
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Appendix. Results by Time of Day
Change from 2013 to 2017 accounts for small number of Uber trips in 2013 (estimated as 1% of taxi trips) and trips dispatched by TNCs to black cars. Data are for June weekdays in 2013 and 2017, for trips starting and/or ending in Manhattan below 60th Street (CBD).
Total
8 a.m.- 8 a.m.- 8 a.m.- 3 p.m.- 7 p.m. 24 hours 7 p.m. 7 p.m. 3 p.m. 7 p.m. midnight
Taxi 2013
Trips 378,166 198,948 18,086 19,096 16,319 19,553
Mileage 1,053,021 489,496 44,500 47,025 40,080 51,203
Total hours 103,404 59,446 5,404 5,805 4,703 5,404
Occupied hours 69,256 41,390 3,763 3,888 3,544 3,704
Unoccupied hours 34,148 18,056 1,641 1,917 1,159 1,700
Pct occupied 67% 70% 70% 67% 75% 69%
Taxi 2017
Trips 249,767 136,851 12,441 12,479 12,374 13,044
Mileage 695,545 326,834 29,712 29,352 30,342 33,595
Total hours 81,087 48,178 4,380 4,382 4,377 4,235
Occupied hours 52,546 33,149 3,014 2,967 3,095 2,837
Unoccupied hours 28,541 15,029 1,366 1,415 1,281 1,398
Pct occupied 65% 69% 69% 68% 71% 67%
TNC 2017
Trips 202,262 105,779 9,616 8,879 10,906 10,379
Mileage 802,135 353,964 32,179 29,538 36,800 38,194
Total hours 91,608 51,929 4,721 4,386 5,307 4,743
Occupied hours 55,069 33,155 3,014 2,762 3,456 2,912
Unoccupied hours 36,539 18,774 1,707 1,624 1,851 1,831
Pct occupied 60% 64% 64% 63% 65% 61%
Total Taxi+TNC 2017
Trips 452,029 242,630 22,057 21,358 23,281 23,423
Mileage 1,497,680 680,798 61,891 58,890 67,141 71,789
Total hours 172,695 100,107 9,101 8,768 9,683 8,979
Occupied hours 107,615 66,304 6,028 5,729 6,551 5,749
Unoccupied hours 65,080 33,802 3,073 3,039 3,132 3,229
Pct occupied 62% 66% 66% 65% 68% 64%
Change 2013 to 2017*
Trips 56,045 34,528 3,138 1,465 6,067 2,958
Mileage 378,464 161,843 14,763 9,377 24,194 17,436
Total hours 61,900 36,463 3,322 2,605 4,577 3,193
Occupied hours 33,844 22,199 2,023 1,613 2,740 1,807
Unoccupied hours 28,056 14,263 1,299 992 1,837 1,386
Pct change: Trips 15% 17% 17% 8% 37% 15%
Pct change: Mileage 36% 33% 33% 20% 60% 34%
Pct change: Total hours 59% 61% 61% 44% 96% 59%
Pct change: Occupied hrs. 48% 53% 53% 41% 77% 48%
Pct change: Unoccup. hrs. 81% 78% 78% 51% 157% 81%
Average per hour
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Trips by Hour: Total taxi/TNC trips in the CBD, by hour, weekdays June 2013 and June 2017
Trips beginning and/or ending in the CBD, average weekday in June 2013 and June 2017.
Vehicles by Hour: Total Taxi/TNC vehicles in the CBD, by hour, weekdays June 2013 and June 2017
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Endnotes
1 Marc Santora, "Cuomo Calls Manhattan Traffic Plan an Idea 'Whose Time Has Come,'" New York Times, August 13, 2017. 2 Office of Governor Andrew Cuomo, " Governor Cuomo Announces "Fix NYC" Advisory Panel," press release, October 5, 2017. 3 Office of Mayor Bill de Blasio, " Mayor de Blasio Announces Initiatives To Help Ease Congestion," press release, October 22, 2017. 4 Schaller Consulting, “Unsustainable? The Growth of App-Based Ride Services and Traffic, Travel and the Future of New York City,” February 2017. 5 Based on the number of Uber vehicles licensed in June 2013, Uber trip volumes were about 1 percent of those for taxis. Uber was the only TNC operating in New York City at that time. 6 For taxis, late March and early April 2013 and April 2017; for TNCs, March to June 2016. 7 Jonathan V. Hall, John J. Horton and Daniel T. Knoepe, "Labor Market Equilibration: Evidence from Uber," October 11, 2017. Available: http://john-joseph-horton.com/papers/uber_price.pdf 8 Although beyond the scope of this report, it should be noted that the full spectrum of trip fees and taxes need to be addressed when considering the per-trip fee discussed here. TNCs currently pay a sales tax that goes to State, City and MTA coffers on each trip, whereas yellow cab riders are charged a 50-cent per trip fee that is dedicated to the MTA. Changes to the existing tax/fee structure should standardize the charges across all vehicles working for-hire in Manhattan, so as not to advantage yellow cabs or TNCs, or vice versa. 9 Bruce Schaller, “Elasticities for Taxicab Fares and Service Availability,” Transportation, Vol. 26, August 1999. 10 New York City Taxi and Limousine Commission, "Taxi Medallion Increase, Final Environmental Impact Statement," October 2013. 11 New York State Traffic Congestion Mitigation Commission, "Commission Recommendation," January 31, 2008. 12 Note that utilization does change when the fare changes. Uber's fare reductions have led to higher utilization with the result, according to the modeling of trip data reported in this paper, that driver incomes stabilize after a fare cut at about the same level as prior to the fare reduction. 13 Although this report focuses on the Manhattan CBD, notably, unoccupied time and customer wait times are about the same throughout the five boroughs of New York City. There are thus excessive number of drivers waiting for customers in Queens and Brooklyn, for example, just as in Manhattan. This report focuses on Manhattan, however, as the center of the city's congestion problem. 14 Tom Krisher, "Waymo rolls out autonomous vans without human backup drivers," Denver Post, November 12, 2017; and Alexandria Sage, Paul Lienert, " GM plans large-scale launch of self-driving cars in U.S. cities in 2019," Reuters, November 30, 2017. 15 International Transport Forum, Shared Mobility: Innovation for Livable Cities. 16 Comparison of taxi/TNC and bus ridership is based on TLC trip data and MTA bus ridership for New York City. National figure is based on and published estimates of Lyft ridership and market share and national bus ridership from the American Public Transportation Association.